Object Detection And Recognition Apparatus Based On CNN Based Integrated Circuits

ABSTRACT

A deep learning object detection and recognition system contains a number of cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together via the network bus. The system is configured for detecting and then recognizing one or more objects out of a two-dimensional (2-D) imagery data. The 2-D imagery data is divided into N set of distinct sub-regions in accordance with respective N partition schemes. CNN based ICs are dynamically allocated for extracting features out of each sub-region for detecting and then recognizing an object potentially contained therein. Any two of the N sets of sub-regions overlap each other. N is a positive integer. Object detection is achieved with a two-category classification using a deep learning model based on approximated fully-connected layers, while object recognition is performed using a local database storing feature vectors of known objects.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part (CIP) to a co-pending U.S.patent application Ser. No. 15/880,375 for “Convolution Layers UsedDirectly For Feature Extraction With A CNN Based Integrated Circuit”filed on Jan. 25, 2018, which is a CIP to a U.S. patent application Ser.No. 15/289,726 for “Digital Integrated Circuit For Extracting FeaturesOut Of An Input Image Based On Cellular Neural Networks” filed on Oct.10, 2016 and issued as U.S. Pat. No. 9,940,534 on Apr. 10, 2018. Thisapplication is also a CIP to a co-pending U.S. patent application Ser.No. 15/920,842 for “Approximating Fully-Connected Layers With MultipleArrays Of 3×3 Convolutional Filter Kernels In A CNN Based IntegratedCircuit” filed on Mar. 14, 2018. All of which are hereby incorporated byreference in their entirety for all purposes.

FIELD

The invention generally relates to the field of machine learning andmore particularly to object detection and recognition apparatus based onCellular Neural Networks (CNN) based digital integrated circuits (ICs).

BACKGROUND

Cellular Neural Networks or Cellular Nonlinear Networks (CNN) have beenapplied to many different fields and problems including, but limited to,image processing since 1988. However, most of the prior art CNNapproaches are either based on software solutions (e.g., ConvolutionalNeural Networks, Recurrent Neural Networks, etc.) or based on hardwarethat are designed for other purposes (e.g., graphic processing, generalcomputation, etc.). As a result, CNN prior approaches are too slow interm of computational speed and/or too expensive thereby impractical forprocessing large amount of imagery data. The imagery data can be fromany two-dimensional data (e.g., still photo, picture, a frame of a videostream, converted form of voice data, etc.). One of the solutions is toperform convolutional operations in a hardware, for example, ApplicationSpecific Integrated Circuit (ASIC).

Practical usages of image processing in a deep learning include, but arenot limited to, object detection, object recognition, etc. Prior artsolutions have been software based solutions, which contain problem withslowness and require large computation resources. Sometimes, thesolution requires many remote computers connected with a either wired orwireless network.

Therefore, it would be desirable to have an improved deep learningobject detection and recognition system that overcomes theaforementioned shortcomings, setbacks and/or problems.

SUMMARY

This section is for the purpose of summarizing some aspects of theinvention and to briefly introduce some preferred embodiments.Simplifications or omissions in this section as well as in the abstractand the title herein may be made to avoid obscuring the purpose of thesection. Such simplifications or omissions are not intended to limit thescope of the invention.

Deep learning object detection and recognition apparatus based on CNNbased ICs are disclosed. According to one aspect of the disclosure, adeep learning object detection and recognition system contains a numberof cellular neural networks (CNN) based integrated circuits (ICs)operatively coupling together via the network bus. The system isconfigured for detecting and then recognizing one or more objects out ofa two-dimensional (2-D) imagery data. The 2-D imagery data is dividedinto N set of distinct sub-regions in accordance with respective Npartition schemes. CNN based ICs are dynamically allocated forextracting features out of each sub-region for detecting and thenrecognizing an object potentially contained therein. Any two of the Nsets of sub-regions overlap each other. N is a positive integer.

According to another aspect of the disclosure, each CNN based ICcontains cellular neural networks (CNN) processing engines operativelycoupled to at least one input/output data bus. The CNN processingengines are connected in a loop with a clock-skew circuit. Each CNNprocessing engine includes a CNN processing block and first and secondsets of memory buffers. CNN processing block is configured forsimultaneously obtaining convolutional operations results using acorresponding portion of a particular sub-region in the 2-D imagery dataand pre-trained filter coefficients in a deep learning model. The firstset of memory buffers operatively couples to the CNN processing blockfor storing the corresponding portion. The second set of memory buffersoperative couples to the CNN processing block for storing thepre-trained filter coefficients.

According to yet another aspect of the disclosure, object detection isachieved with a two-category classification using a deep learning modelbased on approximated fully-connected layers, while object recognitionis performed using a local database storing feature vectors of knownobjects.

Objects, features, and advantages of the invention will become apparentupon examining the following detailed description of an embodimentthereof, taken in conjunction with the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the invention willbe better understood with regard to the following description, appendedclaims, and accompanying drawings as follows:

FIGS. 1A-1B are block diagrams illustrating an example integratedcircuit designed for extracting features from input imagery data inaccordance with one embodiment of the invention;

FIG. 2 is a function block diagram showing an example controllerconfigured for controlling operations of one or more CNN processingengines according to an embodiment of the invention;

FIG. 3 is a diagram showing an example CNN processing engine inaccordance with one embodiment of the invention;

FIG. 4 is a diagram showing M×M pixel locations within a (M+2)-pixel by(M+2)-pixel region, according to an embodiment of the invention;

FIGS. 5A-5C are diagrams showing three example pixel locations,according to an embodiment of the invention;

FIG. 6 is a diagram illustrating an example data arrangement forperforming 3×3 convolutions at a pixel location, according to oneembodiment of the invention;

FIG. 7 is a function block diagram illustrating an example circuitry forperforming 3×3 convolutions at a pixel location, according to oneembodiment of the invention;

FIG. 8 is a diagram showing an example rectification according to anembodiment of the invention;

FIGS. 9A-9B are diagrams showing two example 2×2 pooling operationsaccording to an embodiment of the invention;

FIG. 10 is a diagram illustrating a 2×2 pooling operation reducesM-pixel by M-pixel block to a (M/2)-pixel by (M/2)-pixel block inaccordance with one embodiment of the invention;

FIGS. 11A-11C are diagrams illustrating examples of M-pixel by M-pixelblocks and corresponding (M+2)-pixel by (M+2)-pixel region in an inputimage, according to one embodiment of the invention;

FIG. 12 is a diagram illustrating an example of a first set of memorybuffers for storing received imagery data in accordance with anembodiment of the invention;

FIG. 13A is a diagram showing two operational modes of an example secondset of memory buffers for storing filter coefficients in accordance withan embodiment of the invention;

FIG. 13B is a diagram showing example storage schemes of filtercoefficients in the second set of memory buffers, according to anembodiment of the invention;

FIG. 14 is a diagram showing a plurality of CNN processing enginesconnected as a loop via an example clock-skew circuit in accordance ofan embodiment of the invention;

FIG. 15 is a schematic diagram showing an example image processingtechnique based on convolutional neural networks in accordance with anembodiment of the invention;

FIG. 16 is a flowchart illustrating an example process of achieving atrained convolutional neural networks model having bi-valued 3×3 filterkernels in accordance with an embodiment of the invention;

FIG. 17 is a diagram showing an example filter kernel conversion schemein accordance with the invention;

FIG. 18 is a diagram showing an example data conversion scheme;

FIG. 19 is a diagram showing an example two-dimensional (2-D) imagerydata containing objects to be detected and then recognition inaccordance with an embodiment of the invention;

FIGS. 20A-20B are diagrams showing example N partition schemes fordividing the 2-D imagery data in accordance with an embodiment of theinvention;

FIG. 21 is a diagram showing an example deep learning object detectionand recognition system according to one embodiment of the invention;

FIG. 22 is a diagram showing an example sequence for feature extractionin a deep learning model according to an embodiment of the invention;

FIG. 23 is a diagram illustrating an example deep learning model forobject detection and then recognition in accordance with an embodimentof the invention;

FIG. 24 is a diagram showing example sequence of object detection andthen recognition in accordance with an embodiment of the invention; and

FIG. 25 is a schematic diagram showing example arrays of 3×3 filterkernels for approximating operations of FC layers in accordance with anembodiment of the invention.

DETAILED DESCRIPTIONS

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the invention. However, itwill become obvious to those skilled in the art that the invention maybe practiced without these specific details. The descriptions andrepresentations herein are the common means used by those experienced orskilled in the art to most effectively convey the substance of theirwork to others skilled in the art. In other instances, well-knownmethods, procedures, and components have not been described in detail toavoid unnecessarily obscuring aspects of the invention.

Reference herein to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment can be included in at least one embodiment of theinvention. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Further, the order of blocks in processflowcharts or diagrams or circuits representing one or more embodimentsof the invention do not inherently indicate any particular order norimply any limitations in the invention. Used herein, the terms “top”,“bottom”, “right” and “left” are intended to provide relative positionsfor the purposes of description, and are not intended to designate anabsolute frame of reference

Embodiments of the invention are discussed herein with reference toFIGS. 1A-25. However, those skilled in the art will readily appreciatethat the detailed description given herein with respect to these figuresis for explanatory purposes as the invention extends beyond theselimited embodiments.

Referring first to FIG. 1A, it is shown a block diagram illustrating anexample digital integrated circuit (IC) 100 for extracting features outof an input image in accordance with one embodiment of the invention.

The integrated circuit 100 is implemented as a digital semi-conductorchip and contains a CNN processing engine controller 110, and one ormore neural networks (CNN) processing engines 102 operatively coupled toat least one input/output (I/O) data bus 120. Controller 110 isconfigured to control various operations of the CNN processing engines102 for extracting features out of an input image based on an imageprocessing technique by performing multiple layers of 3×3 convolutionswith rectifications or other nonlinear operations (e.g., sigmoidfunction), and 2×2 pooling operations. To perform 3×3 convolutionsrequires imagery data in digital form and corresponding filtercoefficients, which are supplied to the CNN processing engine 102 viainput/output data bus 120. It is well known that digital semi-conductorchip contains logic gates, multiplexers, register files, memories, statemachines, etc.

According to one embodiment, the digital integrated circuit 100 isextendable and scalable. For example, multiple copy of the digitalintegrated circuit 100 can be implemented on one semiconductor chip.

All of the CNN processing engines are identical. For illustrationsimplicity, only few (i.e., CNN processing engines 122 a-122 h, 132a-132 h) are shown in FIG. 1B. The invention sets no limit to the numberof CNN processing engines on a digital semi-conductor chip.

Each CNN processing engine 122 a-122 h, 132 a-132 h contains a CNNprocessing block 124, a first set of memory buffers 126 and a second setof memory buffers 128. The first set of memory buffers 126 is configuredfor receiving imagery data and for supplying the already receivedimagery data to the CNN processing block 124. The second set of memorybuffers 128 is configured for storing filter coefficients and forsupplying the already received filter coefficients to the CNN processingblock 124. In general, the number of CNN processing engines on a chip is2^(n), where n is an integer (i.e., 0, 1, 2, 3, . . . ). As shown inFIG. 1B, CNN processing engines 122 a-122 h are operatively coupled to afirst input/output data bus 130 a while CNN processing engines 132 a-132h are operatively coupled to a second input/output data bus 130 b. Eachinput/output data bus 130 a-130 b is configured for independentlytransmitting data (i.e., imagery data and filter coefficients). In oneembodiment, the first and the second sets of memory buffers compriserandom access memory (RAM). Each of the first and the second sets arelogically defined. In other words, respective sizes of the first and thesecond sets can be reconfigured to accommodate respective amounts ofimagery data and filter coefficients.

The first and the second I/O data bus 130 a-130 b are shown here toconnect the CNN processing engines 122 a-122 h, 132 a-132 h in asequential scheme. In another embodiment, the at least one I/O data busmay have different connection scheme to the CNN processing engines toaccomplish the same purpose of parallel data input and output forimproving performance.

FIG. 2 is a diagram showing an example controller 200 for controllingvarious operations of at least one CNN processing engine configured onthe integrated circuit. Controller 200 comprises circuitry to controlimagery data loading control 212, filter coefficients loading control214, imagery data output control 216, and image processing operationscontrol 218. Controller 200 further includes register files 220 forstoring the specific configuration (e.g., number of CNN processingengines, number of input/output data bus, etc.) in the integratedcircuit.

Image data loading control 212 controls loading of imagery data torespective CNN processing engines via the corresponding I/O data bus.Filter coefficients loading control 214 controls loading of filtercoefficients to respective CNN processing engines via corresponding I/Odata bus. Imagery data output control 216 controls output of the imagerydata from respective CNN processing engines via corresponding I/O databus. Image processing operations control 218 controls various operationssuch as convolutions, rectifications and pooling operations which can bedefined by user of the integrated circuit via a set of user defineddirectives (e.g., file contains a series of operations such asconvolution, rectification, pooling, etc.).

More details of a CNN processing engine 302 are shown in FIG. 3. A CNNprocessing block 304 contains digital circuitry that simultaneouslyobtains M×M convolution operations results by performing 3×3convolutions at M×M pixel locations using imagery data of a (M+2)-pixelby (M+2)-pixel region and corresponding filter coefficients from therespective memory buffers. The (M+2)-pixel by (M+2)-pixel region isformed with the M×M pixel locations as an M-pixel by M-pixel centralportion plus a one-pixel border surrounding the central portion. M is apositive integer. In one embodiment, M equals to 14 and therefore, (M+2)equals to 16, M×M equals to 14×14=196, and M/2 equals 7.

FIG. 4 is a diagram showing a diagram representing (M+2)-pixel by(M+2)-pixel region 410 with a central portion of M×M pixel locations 420used in the CNN processing engine 302.

Imagery data may represent characteristics of a pixel in the input image(e.g., one of the color (e.g., RGB (red, green, blue)) values of thepixel, or distance between pixel and observing location). Generally, thevalue of the RGB is an integer between 0 and 255. Values of filtercoefficients are floating point integer numbers that can be eitherpositive or negative.

In order to achieve faster computations, few computational performanceimprovement techniques have been used and implemented in the CNNprocessing block 304. In one embodiment, representation of imagery datauses as few bits as practical (e.g., 5-bit representation). In anotherembodiment, each filter coefficient is represented as an integer with aradix point. Similarly, the integer representing the filter coefficientuses as few bits as practical (e.g., 12-bit representation). As aresult, 3×3 convolutions can then be performed using fixed-pointarithmetic for faster computations.

Each 3×3 convolution produces one convolution operations result, Out(m,n), based on the following formula:

$\begin{matrix}{{{Out}\left( {m,n} \right)} = {{\sum\limits_{{1 \leq i},{j \leq 3}}\mspace{14mu} {{{In}\left( {m,n,i,j} \right)} \times {C\left( {i,j} \right)}}} - b}} & (1)\end{matrix}$

where:

-   -   m, n are corresponding row and column numbers for identifying        which imagery data (pixel) within the (M+2)-pixel by (M+2)-pixel        region the convolution is performed;    -   In(m,n,i,j) is a 3-pixel by 3-pixel area centered at pixel        location (m, n) within the region;    -   C(i, j) represents one of the nine weight coefficients C(3×3),        each corresponds to one of the 3-pixel by 3-pixel area;    -   b represents an offset coefficient; and    -   i,j are indices of weight coefficients C(i, j).

Each CNN processing block 304 produces M×M convolution operationsresults simultaneously and, all CNN processing engines performsimultaneous operations.

FIGS. 5A-5C show three different examples of the M×M pixel locations.The first pixel location 531 shown in FIG. 5A is in the center of a3-pixel by 3-pixel area within the (M+2)-pixel by (M+2)-pixel region atthe upper left corner. The second pixel location 532 shown in FIG. 5B isone pixel data shift to the right of the first pixel location 531. Thethird pixel location 533 shown in FIG. 5C is a typical example pixellocation. M×M pixel locations contain multiple overlapping 3-pixel by3-pixel areas within the (M+2)-pixel by (M+2)-pixel region.

To perform 3×3 convolutions at each sampling location, an example dataarrangement is shown in FIG. 6. Imagery data (i.e., In(3×3)) and filtercoefficients (i.e., weight coefficients C(3×3) and an offset coefficientb) are fed into an example CNN 3×3 circuitry 600. After 3×3 convolutionsoperation in accordance with Formula (1), one output result (i.e.,Out(1×1)) is produced. At each sampling location, the imagery dataIn(3×3) is centered at pixel coordinates (m, n) 605 with eight immediateneighbor pixels 601-604, 606-609.

FIG. 7 is a function diagram showing an example CNN 3×3 circuitry 700for performing 3×3 convolutions at each pixel location. The circuitry700 contains at least adder 721, multiplier 722, shifter 723, rectifier724 and pooling operator 725. In a digital semi-conductorimplementation, all of these can be achieved with logic gates andmultiplexers, which are generated using well-known methods (e.g.,hardware description language such as Verilog, etc.). Adder 721 andmultiplier 722 are used for addition and multiplication operations.Shifter 723 is for shifting the output result in accordance withfixed-point arithmetic involved in the 3×3 convolutions. Rectifier 724is for setting negative output results to zero. Pooling operator 725 isfor performing 2×2 pooling operations.

Imagery data are stored in a first set of memory buffers 306, whilefilter coefficients are stored in a second set of memory buffers 308.Both imagery data and filter coefficients are fed to the CNN block 304at each clock of the digital integrated circuit. Filter coefficients(i.e., C(3×3) and b) are fed into the CNN processing block 304 directlyfrom the second set of memory buffers 308. However, imagery data are fedinto the CNN processing block 304 via a multiplexer MUX 305 from thefirst set of memory buffers 306. Multiplexer 305 selects imagery datafrom the first set of memory buffers based on a clock signal (e.g.,pulse 312).

Otherwise, multiplexer MUX 305 selects imagery data from a firstneighbor CNN processing engine (from the left side of FIG. 3 not shown)through a clock-skew circuit 320.

At the same time, a copy of the imagery data fed into the CNN processingblock 304 is sent to a second neighbor CNN processing engine (to theright side of FIG. 3 not shown) via the clock-skew circuit 320.Clock-skew circuit 320 can be achieved with known techniques (e.g., a Dflip-flop 322).

The first neighbor CNN processing engine may be referred to as anupstream neighbor CNN processing engine in the loop formed by theclock-skew circuit 320. The second neighbor CNN processing engine may bereferred to as a downstream CNN processing engine. In anotherembodiment, when the data flow direction of the clock-skew circuit isreversed, the first and the second CNN processing engines are alsoreversed becoming downstream and upstream neighbors, respectively.

After 3×3 convolutions for each group of imagery data are performed forpredefined number of filter coefficients, convolution operations resultsOut(m, n) are sent to the first set of memory buffers via anothermultiplex MUX 307 based on another clock signal (e.g., pulse 311). Anexample clock cycle 310 is drawn for demonstrating the time relationshipbetween pulse 311 and pulse 312. As shown pulse 311 is one clock beforepulse 312, as a result, the 3×3 convolution operations results arestored into the first set of memory buffers after a particular block ofimagery data has been processed by all CNN processing engines throughthe clock-skew circuit 320.

After the convolution operations result Out(m, n) is obtained fromFormula (1), rectification procedure may be performed as directed byimage processing control 218. Any convolution operations result, Out(m,n), less than zero (i.e., negative value) is set to zero. In otherwords, only positive value of output results are kept. FIG. 8 shows twoexample outcomes of rectification. A positive output value 10.5 retainsas 10.5 while −2.3 becomes 0. Rectification causes non-linearity in theintegrated circuits.

If a 2×2 pooling operation is required, the M×M output results arereduced to (M/2)×(M/2). In order to store the (M/2)×(M/2) output resultsin corresponding locations in the first set of memory buffers,additional bookkeeping techniques are required to track proper memoryaddresses such that four (M/2)×(M/2) output results can be processed inone CNN processing engine.

To demonstrate a 2×2 pooling operation, FIG. 9A is a diagram graphicallyshowing first example output results of a 2-pixel by 2-pixel block beingreduced to a single value 10.5, which is the largest value of the fouroutput results. The technique shown in FIG. 9A is referred to as “maxpooling”. When the average value 4.6 of the four output results is usedfor the single value shown in FIG. 9B, it is referred to as “averagepooling”. There are other pooling operations, for example, “mixed maxaverage pooling” which is a combination of “max pooling” and “averagepooling”. The main goal of the pooling operation is to reduce the sizeof the imagery data being processed. FIG. 10 is a diagram illustratingM×M pixel locations, through a 2×2 pooling operation, being reduced to(M/2)×(M/2) locations, which is one fourth of the original size.

An input image generally contains a large amount of imagery data. Inorder to perform image processing operations. The input image 1100 ispartitioned into M-pixel by M-pixel blocks 1111-1112 as shown in FIG.11A. Imagery data associated with each of these M-pixel by M-pixelblocks is then fed into respective CNN processing engines. At each ofthe M×M pixel locations in a particular M-pixel by M-pixel block, 3×3convolutions are simultaneously performed in the corresponding CNNprocessing block.

Although the invention does not require specific characteristicdimension of an input image, the input image may be required to resizeto fit into a predefined characteristic dimension for certain imageprocessing procedures. In an embodiment, a square shape with(2^(K)×M)-pixel by (2^(K)×M)-pixel is required. K is a positive integer(e.g., 1, 2, 3, 4, etc.). When M equals 14 and K equals 4, thecharacteristic dimension is 224. In another embodiment, the input imageis a rectangular shape with dimensions of (2¹×M)-pixel and (2¹×M)-pixel,where I and J are positive integers.

In order to properly perform 3×3 convolutions at pixel locations aroundthe border of a M-pixel by M-pixel block, additional imagery data fromneighboring blocks are required. FIG. 11B shows a typical M-pixel byM-pixel block 1120 (bordered with dotted lines) within a (M+2)-pixel by(M+2)-pixel region 1130. The (M+2)-pixel by (M+2)-pixel region is formedby a central portion of M-pixel by M-pixel from the current block, andfour edges (i.e., top, right, bottom and left) and four corners (i.e.,top-left, top-right, bottom-right and bottom-left) from correspondingneighboring blocks. Additional details are shown in FIG. 12 andcorresponding descriptions for the first set of memory buffers.

FIG. 11C shows two example M-pixel by M-pixel blocks 1122-1124 andrespective associated (M+2)-pixel by (M+2)-pixel regions 1132-1134.These two example blocks 1122-1124 are located along the perimeter ofthe input image. The first example M-pixel by M-pixel block 1122 islocated at top-left corner, therefore, the first example block 1122 hasneighbors for two edges and one corner. Value “0”s are used for the twoedges and three corners without neighbors (shown as shaded area) in theassociated (M+2)-pixel by (M+2)-pixel region 1132 for forming imagerydata. Similarly, the associated (M+2)-pixel by (M+2)-pixel region 1134of the second example block 1124 requires “0”s be used for the top edgeand two top corners. Other blocks along the perimeter of the input imageare treated similarly. In other words, for the purpose to perform 3×3convolutions at each pixel of the input image, a layer of zeros (“0”s)is added outside of the perimeter of the input image. This can beachieved with many well-known techniques. For example, default values ofthe first set of memory buffers are set to zero. If no imagery data isfilled in from the neighboring blocks, those edges and corners wouldcontain zeros.

Furthermore, an input image can contain a large amount of imagery data,which may not be able to be fed into the CNN processing engines in itsentirety. Therefore, the first set of memory buffers is configured onthe respective CNN processing engines for storing a portion of theimagery data of the input image. The first set of memory bufferscontains nine different data buffers graphically illustrated in FIG. 12.Nine buffers are designed to match the (M+2)-pixel by (M+2)-pixel regionas follows:

1) buffer-0 for storing M×M pixels of imagery data representing thecentral portion;2) buffer-1 for storing 1×M pixels of imagery data representing the topedge;3) buffer-2 for storing M×1 pixels of imagery data representing theright edge;4) buffer-3 for storing 1×M pixels of imagery data representing thebottom edge;5) buffer-4 for storing M×1 pixels of imagery data representing the leftedge;6) buffer-5 for storing 1×1 pixels of imagery data representing the topleft corner;7) buffer-6 for storing 1×1 pixels of imagery data representing the topright corner;8) buffer-7 for storing 1×1 pixels of imagery data representing thebottom right corner; and9) buffer-8 for storing 1×1 pixels of imagery data representing thebottom left corner.

Imagery data received from the I/O data bus are in form of M×M pixels ofimagery data in consecutive blocks. Each M×M pixels of imagery data isstored into buffer-0 of the current block. The left column of thereceived M×M pixels of imagery data is stored into buffer-2 of previousblock, while the right column of the received M×M pixels of imagery datais stored into buffer-4 of next block. The top and the bottom rows andfour corners of the received M×M pixels of imagery data are stored intorespective buffers of corresponding blocks based on the geometry of theinput image (e.g., FIGS. 11A-11C).

An example second set of memory buffers for storing filter coefficientsare shown in FIG. 13A. In one embodiment, a pair of independent buffersBuffer0 1301 and Buffer1 1302 is provided. The pair of independentbuffers allow one of the buffers 1301-1302 to receive data from the I/Odata bus 1330 while the other one to feed data into a CNN processingblock (not shown). Two operational modes are shown herein.

Example storage schemes of filter coefficients are shown in FIG. 13B.Each of the pair of buffers (i.e., Buffer0 1301 or Buffer1 1302) has awidth (i.e., word size 1310). In one embodiment, the word size is120-bit. Accordingly, each of the filter coefficients (i.e., C(3×3) andb) occupies 12-bit in the first example storage scheme 1311. In thesecond example storage scheme 1312, each filter coefficient occupies6-bit thereby 20 coefficients are stored in each word. In the thirdexample scheme 1313, 3-bit is used for each coefficient hence four setsof filter coefficients (40 coefficients) are stored. Finally, in thefourth example storage scheme 1314, 80 coefficients are stored in eachword, each coefficient occupies 1.5-bit.

In another embodiment, a third memory buffer can be set up for storingentire filter coefficients to avoid I/O delay. In general, the inputimage must be at certain size such that all filter coefficients can bestored. This can be done by allocating some unused capacity in the firstset of memory buffers to accommodate such a third memory buffer. Sinceall memory buffers are logically defined in RAM (Random-Access Memory),well known techniques may be used for creating the third memory buffer.In other words, the first and the second sets of memory buffers can beadjusted to fit different amounts of imagery data and/or filtercoefficients. Furthermore, the total amount of RAM is dependent uponwhat is required in image processing operations.

When more than one CNN processing engine is configured on the integratedcircuit. The CNN processing engine is connected to first and secondneighbor CNN processing engines via a clock-skew circuit. Forillustration simplicity, only CNN processing block and memory buffersfor imagery data are shown. An example clock-skew circuit 1440 for agroup of CNN processing engines are shown in FIG. 14. The CNN processingengines connected via the second example clock-skew circuit 1440 form aloop. In other words, each CNN processing engine sends its own imagerydata to a first neighbor and, at the same time, receives a secondneighbor's imagery data. Clock-skew circuit 1440 can be achieved withwell-known manners. For example, each CNN processing engine is connectedwith a D flip-flop 1442.

A special case with only two CNN processing engines are connected in aloop, the first neighbor and the second neighbor are the same.

Referring now to FIG. 15, it is a schematic diagram showing an exampleimage processing technique based on convolutional neural networks inaccordance with an embodiment of the invention. Based on convolutionalneural networks, multi-layer input imagery data 1511 a-1511 c isprocessed with convolutions using a first set of filters or weights1520. Since the imagery data 1511 a-1511 c is larger than the filters1520. Each corresponding overlapped sub-region 1515 of the imagery datais processed. After the convolutional results are obtained, activationmay be conducted before a first pooling operation 1530. In oneembodiment, activation is achieved with rectification performed in arectified linear unit (ReLU). As a result of the first pooling operation1530, the imagery data is reduced to a reduced set of imagery data 1531a-1531 c. For 2×2 pooling, the reduced set of imagery data is reduced bya factor of 4 from the previous set.

The previous convolution-to-pooling procedure is repeated. The reducedset of imagery data 1531 a-1531 c is then processed with convolutionsusing a second set of filters 1540. Similarly, each overlappedsub-region 1535 is processed. Another activation can be conducted beforea second pooling operation 1540. The convolution-to-pooling proceduresare repeated for several layers and finally connected to aFully-connected (FC) Layers 1560. In image classification, respectiveprobabilities of predefined categories can be computed in FC Layers1560.

This repeated convolution-to-pooling procedure is trained using a knowndataset or database. For image classification, the dataset contains thepredefined categories. A particular set of filters, activation andpooling can be tuned and obtained before use for classifying an imagerydata, for example, a specific combination of filter types, number offilters, order of filters, pooling types, and/or when to performactivation. In one embodiment, convolutional neural networks are basedon Visual Geometry Group (VGG16) architecture neural nets, whichcontains 13 convolutional layers and three fully-connected networklayers.

A trained convolutional neural networks model is achieved with anexample set of operations 1600 shown in FIG. 16. At action 1602, aconvolutional neural networks model is first obtained by training theconvolutional neural networks model based on image classification of alabeled dataset, which contains a sufficiently large number of inputdata (e.g., imagery data, converted voice data, optical characterreorganization (OCR) data, etc.). For example, there are at least 4,000data for each category. In other words, each data in the labeled datasetis associated with a category to be classified. The convolutional neuralnetworks model includes multiple ordered filter groups (e.g., eachfilter group corresponds to a convolutional layer in the convolutionalneural networks model). Each filter in the multiple ordered filtergroups contains a standard 3×3 filter kernel (i.e., nine coefficients infloating point number format (e.g., standard 3×3 filter kernel 1710 inFIG. 17)). Each of the nine coefficients can be any negative or positivereal number (i.e., a number with fraction). The initial convolutionalneural networks model may be obtained from many different frameworksincluding, but not limited to, Mxnet, caffe, tensorflow, etc.

Then, at action 1604, the convolutional neural networks model ismodified by converting respective standard 3×3 filter kernels 1710 tocorresponding bi-valued 3×3 filter kernels 1720 of a currently-processedfilter group in the multiple ordered filter groups based on a set ofkernel conversion schemes. In one embodiment, each of the ninecoefficients C(i,j) in the corresponding bi-valued 3×3 filter kernel1720 is assigned a value ‘A’ which equals to the average of absolutecoefficient values multiplied by the sign of corresponding coefficientsin the standard 3×3 filter kernel 1710 shown in following formula:

$\begin{matrix}{A = {\sum\limits_{{1 \leq i},{j \leq 3}}\left| {C\left( {i,j} \right)} \middle| {\text{/}9} \right.}} & (2)\end{matrix}$

Filter groups are converted one at a time in the order defined in themultiple ordered filter groups. In certain situation, two consecutivefilter groups are optionally combined such that the training of theconvolutional neural networks model is more efficient.

Next, at action 1606, the modified convolutional neural networks modelis retrained until a desired convergence criterion is met or achieved.There are a number of well known convergence criteria including, but notlimited to, completing a predefined number of retraining operation,converging of accuracy loss due to filter kernel conversion, etc. In oneembodiment, all filter groups including those already converted inprevious retraining operations can be changed or altered for finetuning. In another embodiment, the already converted filter groups arefrozen or unaltered during the retraining operation of thecurrently-processed filter group.

Process 1600 moves to decision 1608, it is determined whether there isanother unconverted filter group. If ‘yes’, process 1600 moves back torepeat actions 1604-1606 until all filter groups have been converted.Decision 1608 becomes ‘no’ thereafter. At action 1610, coefficients ofbi-valued 3×3 filter kernels in all filter groups are transformed from afloating point number format to a fixed point number format toaccommodate the data structure required in the CNN based integratedcircuit. Furthermore, the fixed point number is implemented asreconfigurable circuits in the CNN based integrated circuit. In oneembodiment, the coefficients are implemented using 12-bit fixed pointnumber format.

FIG. 18 is a diagram showing an example data conversion scheme forconverting data from 8-bit [0-255] to 5-bit [0-31] per pixel. Forexample, bits 0-7 becomes 0, bits 8-15 becomes 1, etc.

As described in process 1600 of FIG. 16, a convolutional neural networksmodel is trained for the CNN based integrated circuit. The entire set oftrained coefficients or weights are pre-configured to the CNN basedintegrated circuit as a feature extractor for a particular data format(e.g., imagery data, voice spectrum, fingerprint, palm-print, opticalcharacter recognition (OCR), etc.). In general, there are manyconvolutional layers with many filters in each layer. In one embodiment,VGG16 model contains 13 ordered convolutional layers. In a softwarebased image classification task, computations for the convolutionallayers take majority of computations (e.g., 90%) traditionally. Thiscomputations is drastically reduced with a dedicated hardware such asCNN based IC 100.

Referring now to FIG. 19, it is shown an example two-dimensional (2-D)imagery data 1900, which contains objects (e.g., faces) 1910 a-1910 c tobe detected and then recognized. In general, there are three channels(e.g., colors Red, Green and Blue) of the 2-D imagery data 1900. Forillustration simplicity, only one frame is shown. The 2-D imagery datacontains a width 1921 and a height 1922 in terms of pixels of data.

FIGS. 20A-20B are diagrams showing example N partition schemes fordividing a 2-D imagery data 2000. The 2-D imagery data 2000 is dividedinto 16 sub-regions 2020 a in the first partition scheme, while the 2-Dimagery data 2000 is divided into 9 sub-regions 2020 b in the secondpartition scheme, and so on. In the N-th partition scheme, the 2-Dimagery data 2000 is divided into a N-th set of sub-regions 2020 n. Eachsub-region 2020 a, 2020 b, . . . , 2020 n potentially contains an objectfor detection and then recognition. Each sub-region 2020 a, 2020 b, . .. , 2020 n is defined by a width 2021 and a height 2022 in terms ofpixel counts. In one embodiment, the size of each sub-region is definedby user of the deep learning object detection and recognition system. Inanother embodiment, the size may be dynamically adjusted to differenttypes of object for detection and recognition. Any two sets of thesub-regions overlap each other.

FIG. 21 is a diagram shown an example deep learning object detection andrecognition system 2100 based on multiple CNN based ICs 2110 operativelycoupling together via a network bus 2130. Example network bus 2130 canbe a Universal Serial Bus or Peripheral Component Interconnect Expressbus, when connected in a dongle or in a computer system, respectively.CNN based ICs 2110 are dynamically allocated into N groups 2120 a, 2120b, . . . , 2120 n in accordance with respective N partition schemesshown in FIGS. 20A-20B. On example allocation is to map one CNN based ICto each sub-region as a result of N partition schemes, which means thatpotential object detection and recognition within each sub-region isperformed with one particular CNN based IC 2110. In other exampleallocations, one CNN based IC 2110 may be assigned to process twosub-regions or even more. The trade-off is the detection/recognitionspeed versus the number CNN based ICs 2110 (i.e., cost of the system2100). Allocation of CNN based ICs 2110 changes when a new set ofpartition schemes is chosen or used. When an object is detected and thenrecognized in certain sub-regions, CNN based ICs 2110 allocated forthose sub-regions are marked with shading lines in FIG. 21.

FIG. 22 is a diagram showing an example feature extraction task in adeep learning network that includes convolutional layers. Input data(e.g., sub-region of the 2-D imagery data with three basic colorchannels) 2210 is processed through a number of ordered convolutionallayers 2225 in a CNN based integrated circuit (e.g., integrated circuit100 of FIG. 1) 2220. The output 2230 of the last layer of the orderedconvolutional layers contains P channels or feature maps with F×F pixelsof data per feature map. F and P are positive integers or whole numbers.For illustration and description simplicity, activation layers andpooling layers are not shown in the CNN based integrated circuit 2220.In order to effectively and efficiently detecting and then recognitionan object in a sub-region of a 2-D imagery data, P is set to 1. Theresulting feature map 2240 contains F×F pixels of data.

A conversion technique may be used to convert the data format of thecorresponding portion of the particular sub-region for facilitating datastructure of the CNN based IC. For example, each CNN based IC acceptsimagery data with M-pixel×M-pixel shown in FIG. 4. A sub-region withwidth and height that are not conformed with the data structure of theCNN based IC would need a conversion.

Corresponding to the single channel last layer, an example deep learningmodel 2300 is shown in FIG. 23. Deep learning model 2300 derived fromVGG 16 model contains multiple ordered convolutional layers organized byseveral major convolutional layer groups separated by multiple poolinglayers. In this example, the last layer (i.e., Convolutional Layer 5-3)contains only one channel.

FIG. 24 is a diagram showing example sequence of object detection andthen recognition performed in a deep learning objection detection andrecognition system 2400 based on CNN based ICs. The output of the lastlayer of the multiple ordered convolutional layers is a feature map withF×F pixels of data 2410, which can be represented as a feature vector2420 with F×F entries or numbers. A two-category classification task2430 is then performed using the feature vector 2420 to determinewhether an object is detected in the corresponding sub-region. If anobject is detected (shown as “YES”), the feature vector 2420 is comparedwith stored feature vectors of known objects in a local database 2460 inan object recognition task. Once a match has been found, the object 2480is recognized. Local database 2460 is located or included within thedeep learning objection detection and recognition system 2400.

In an alternative embodiment, each CNN based IC is loaded with a deeplearning model using multiple arrays of 3×3 convolutional filter kernelsto approximate fully-connected layers as shown in FIG. 25. Atwo-category classification task is conducted for each sub-region. Forthose sub-region with confirmed detection of an object, the CNN based ICis then loaded with reduced convolutional output channels to extractfeatures for comparison with a local database for object recognition.

FIG. 25 is a schematic diagram showing example multiple 3×3 filterkernels for approximating operations of FC layers. In one embodiment,the arrays of 3×3 filter kernels are implemented in the CNN based IC.Instead of inner-product operations, convolutional operations usingmultiple arrays of 3×3 filter kernels are used. The output 2530 of theconvolutional layers contains P feature maps with F×F pixels of data perfeature map. An array of Q×P of 3×3 filter kernels 2551 is used forconvolutional operations to obtain Q feature maps 2561 with (F−2)×(F−2)pixels of useful data per feature map. Useful data are located in centerportion of respective feature maps, when one-pixel padding is addedaround the perimeter of each feature map for convolutional operations.When no padding scheme is used, the size of feature maps shrinks by twopixels in each direction after each of the L layers. The next layer issubstantial similar to the previous one. R feature maps 2562 with(F−4)×(F−4) pixels of useful data per feature map are obtained using anarray of R×Q of 3×3 filter kernels 2552. The procedure continues untilthe final output contains Z feature maps 2569 with 1×1 pixel of usefuldata per feature map. F, P, Q, R, Z are positive integer. The 1×1 pixelof useful data of the final output can be treated as Z classes for imageclassification.

It is evident that the number of layers to reduce F×F pixels of data to1×1 pixel of useful data requires L layers, where L is equal to (F−1)/2,if F is an odd number. If F is an even number, then certain well-knownpadding techniques need to be applied to the F×F pixels of data toensure the final output contains 1×1 pixel of useful data. For example,L is equal to (F−2)/2 if no padding to the right and bottom sides of thefeature map, or L is equal to F/2 if two-pixel padding to the right andbottom. Any larger number of layers can be achieved with additionalredundancy for more robustness. However, it would require additionalresources (e.g., processing power, storage, etc.). Any smaller number oflayers can be used for computation efficiency. However, it may sacrificesome accuracy.

Although the invention has been described with reference to specificembodiments thereof, these embodiments are merely illustrative, and notrestrictive of, the invention. Various modifications or changes to thespecifically disclosed example embodiments will be suggested to personsskilled in the art. For example, whereas a specific type of deeplearning model has been shown and described, other types of deeplearning model may be used to achieve the same, for example, ResNet,MobileNet, etc. Additionally, three objects have been shown anddescribed in a 2-D imagery data, other number of objects may exist,there is no limitation as to how many potential objects to be detectedand then recognized. Furthermore, whereas example deep learning modelhas been shown with one channel in the last layer, other reduced numberof channels (e.g., two) may be used to achieve substantially similarresults. Finally, whereas the size of each sub-region has been shownwith the same size, different sized sub-regions from different partitionschemes may be used for achieving the same. In summary, the scope of theinvention should not be restricted to the specific example embodimentsdisclosed herein, and all modifications that are readily suggested tothose of ordinary skill in the art should be included within the spiritand purview of this application and scope of the appended claims.

What is claimed is:
 1. A deep learning object detection and recognitionsystem comprising: a network bus; and a plurality of cellular neuralnetworks (CNN) based integrated circuits (ICs) operatively couplingtogether via the network bus and being configured for detecting and thenrecognizing one or more objects out of a two-dimensional (2-D) imagerydata, the 2-D imagery data being divided into N set of distinctsub-regions in accordance with respective N partition schemes and theCNN based ICs being dynamically allocated for extracting features out ofeach sub-region for detecting and then recognizing an object potentiallycontained therein, where N is a positive integer.
 2. The system of claim1, wherein any two of the N sets of sub-regions overlap each other. 3.The system of claim 1, wherein the total number of the sub-regionsequals to the number of the allocated CNN based ICs.
 4. The system ofclaim 1, the total number of the sub-regions equals to an positiveinteger multiple of the number of the allocated CNN based ICs.
 5. Thesystem of claim 1, wherein each of the CNN based ICs comprises aplurality of CNN processing engines operatively coupled to at least oneinput/output data bus, the plurality of CNN processing engines beingconnected in a loop with a clock-skew circuit, each CNN processingengine includes: a CNN processing block configured for simultaneouslyobtaining convolutional operations results using a corresponding portionof a particular sub-region in the 2-D imagery data and pre-trainedfilter coefficients in a deep learning model; a first set of memorybuffers operatively coupling to the CNN processing block for storing thecorresponding portion of the particular sub-region; and a second set ofmemory buffers operative coupling to the CNN processing block forstoring the pre-trained filter coefficients.
 6. The system of claim 5,wherein the deep learning model contains a plurality of orderedconvolutional layers.
 7. The system of claim 6, wherein the deeplearning model further contains layers of pooling and activationoperations.
 8. The system of claim 7, wherein the deep learning modelfurther contains additional multiple 3×3 filter kernels forapproximating operations of fully-connected layers.
 9. The system ofclaim 8, wherein the deep learning model is used for conducting atwo-category classification for object detection.
 10. The system ofclaim 9, wherein last layer of the plurality of ordered convolutionallayers contains a single channel.
 11. The system of claim 10, whereinthe extracted features are organized as a feature vector contained inthe single channel for object recognition.
 12. The system of claim 5,wherein last layer of the plurality of ordered convolutional layerscontains a single channel.
 13. The system of claim 12, wherein theextracted features are organized as a feature vector contained in thesingle channel for object detection and recognition.
 14. The system ofclaim 13, wherein the feature vector is used in a two-categoryclassification task for determining whether an object is detected in theparticular sub-region.
 15. The system of claim 14, wherein the featurevector is compared with stored feature vectors of known objects in alocal database for object recognition after an object has been detected.16. The system of claim 5, wherein the particular sub-region contains awidth and a height in terms of pixel counts.
 17. The system of claim 16,wherein a conversion technique is used to convert data format of thecorresponding portion of the particular sub-region for facilitating datastructure of the CNN based IC.
 18. The system of claim 1, wherein saideach of the CNN based ICs is packaged in a dongle for facilitatingconnection with the network bus that comprises a Universal Serial Bus.19. The system of claim 1, wherein said each of the CNN based ICs isinstalled in a computer system for facilitating connection with thenetwork bus that comprises a Peripheral Component Interconnect Expressbus.